10Pearls vs DataArt: full comparison for 2026
Quick verdict
10Pearls (3.9/5) edges ahead of DataArt (3.9/5) overall. 10Pearls is the better choice for enterprises wanting AI bundled with digital transformation work. DataArt is the stronger option for enterprises in finance or healthcare needing AI at global scale. The right choice depends on your project size, budget, and required tech stack.
10Pearls vs DataArt: head-to-head summary
| Criterion | 10Pearls | DataArt |
|---|---|---|
| Founded | 2004 | 1997 |
| HQ | Vienna, United States | New York, United States |
| Team size | 1,800-1,950 | 5,700+ |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Primary differentiator | Two decades of digital transformation delivery with AI as an established add-on practice | Nearly 30 years of engineering history across 30-plus global delivery locations |
| Pricing model | Dedicated team or retainer | Dedicated team or retainer |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, AWS, Azure | Python, AWS, Azure |
| Industries served | Financial services, Healthcare, Retail & e-commerce | Financial services, Healthcare, Media & entertainment, Travel & hospitality |
10Pearls vs DataArt: overview
10Pearls
10Pearls was founded in 2004 by brothers Imran and Zeeshan Aftab, and is headquartered in Vienna, Virginia. The firm operates across six countries with roughly 1,800-1,950 employees depending on the reporting period, and one source cites revenue near $358 million in 2024. Its core business is software development, product design, and digital transformation broadly, with AI development positioned as one service line within that larger practice rather than the firm's defining specialty.
DataArt
DataArt was founded in 1997 by Eugene Goland and is headquartered in New York City, with roughly 5,700 employees spread across more than 30 locations in the US, Europe, the UK, Latin America, and the UAE. The firm delivers data, analytics, and AI platforms for finance, media and entertainment, healthcare and life sciences, retail, and travel and hospitality clients. Nearly three decades of operating history gives it a longer track record than almost every other firm on this list, though AI is delivered as part of a broader software engineering practice rather than a standalone specialty.
Services and capabilities: 10Pearls vs DataArt
| Capability | 10Pearls | DataArt |
|---|---|---|
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| AI consulting | ✗ | ✗ |
| Fixed-price projects | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: 10Pearls vs DataArt
| Framework / platform | 10Pearls | DataArt |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Kubernetes | ✓ | ✓ |
Pricing comparison: 10Pearls vs DataArt
| Criterion | 10Pearls | DataArt |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Retainer | Dedicated team, Retainer |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: 10Pearls vs DataArt
| Dimension | 10Pearls | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Healthcare, Retail & e-commerce | Financial services, Healthcare, Media & entertainment |
| Best use cases | Bundling an AI initiative into a larger digital transformation contract., Needing a financially stable US vendor for a multi-year enterprise engagement. | Building AI-driven analytics platforms for finance or healthcare clients with strict compliance needs., Running a long-term AI and data engineering program with a financially established vendor. |
| Typical project type | Dedicated team | Dedicated team |
10Pearls vs DataArt: pros and cons
| 10Pearls | |
|---|---|
| + | Reported revenue near $358 million signals financial stability for long engagements. |
| + | Twenty-plus years of digital transformation delivery experience. |
| + | US headquarters simplifies contracting for domestic enterprise buyers. |
| + | Six-country delivery footprint supports round-the-clock development cycles. |
| - | AI is one of several service lines rather than the firm's primary specialty |
| - | Scale means engagement minimums are typically higher than boutique AI firms |
| DataArt | |
|---|---|
| + | Nearly three decades of software engineering history, among the longest on this list. |
| + | 5,700-plus employees across 30-plus locations globally. |
| + | Named industry focus areas (finance, healthcare, travel) show real vertical depth. |
| + | Data and analytics platform experience supports AI work that needs solid data foundations. |
| - | AI sits inside a much broader software engineering practice rather than being the firm's core identity |
| - | Enterprise scale typically means slower onboarding than smaller, more agile AI boutiques |
Who should choose 10Pearls?
A typical fit: bundling an AI initiative into a larger digital transformation contract.
Two decades of digital transformation delivery with AI as an established add-on practice. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce.
Who should choose DataArt?
A typical fit: building AI-driven analytics platforms for finance or healthcare clients with strict compliance needs.
Nearly 30 years of engineering history across 30-plus global delivery locations. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Media & entertainment, Travel & hospitality.
Decision matrix: 10Pearls vs DataArt
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | 10Pearls |
| Your budget is at the lower end | Compare: 10Pearls (Not disclosed) vs DataArt (Not disclosed) |
| You need specialist depth in a specific vertical | DataArt |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: 10Pearls vs DataArt
| Use case | 10Pearls fit | DataArt fit | Winner |
|---|---|---|---|
| Bundling an AI initiative into a larger digital transformation contract. | Strong | Limited | 10Pearls |
| Needing a financially stable US vendor for a multi-year enterprise engagement. | Strong | Strong | Both equally |
| Building AI-driven analytics platforms for finance or healthcare clients with strict compliance needs. | Limited | Strong | DataArt |
| Running a long-term AI and data engineering program with a financially established vendor. | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: 10Pearls vs DataArt
10Pearls (3.9/5) is the stronger overall choice for most AI Development projects. Two decades of digital transformation delivery with AI as an established add-on practice.
DataArt (3.9/5) is worth a look if you need running a long-term AI and data engineering program with a financially established vendor. If your situation matches that, DataArt is a competitive option.
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10Pearls vs DataArt FAQ
Is 10Pearls better than DataArt?
10Pearls (3.9/5) scores higher overall, but "better" depends on your use case. 10Pearls's strongest advantage: reported revenue near $358 million signals financial stability for long engagements. DataArt's strongest advantage: nearly three decades of software engineering history, among the longest on this list.
How do 10Pearls and DataArt differ in pricing?
10Pearls uses dedicated team or retainer pricing. DataArt uses dedicated team or retainer pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: 10Pearls or DataArt?
10Pearls is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between 10Pearls and DataArt?
10Pearls's primary differentiator is: two decades of digital transformation delivery with AI as an established add-on practice. DataArt's primary differentiator is: nearly 30 years of engineering history across 30-plus global delivery locations. They also differ in team size (1,800-1,950 vs 5,700+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Financial services, Healthcare).
Verify all details directly with each company before making a decision.